English

Entity Embeddings : Perspectives Towards an Omni-Modality Era for Large Language Models

Machine Learning 2023-10-31 v1

Abstract

Large Language Models (LLMs) are evolving to integrate multiple modalities, such as text, image, and audio into a unified linguistic space. We envision a future direction based on this framework where conceptual entities defined in sequences of text can also be imagined as modalities. Such a formulation has the potential to overcome the cognitive and computational limitations of current models. Several illustrative examples of such potential implicit modalities are given. Along with vast promises of the hypothesized structure, expected challenges are discussed as well.

Keywords

Cite

@article{arxiv.2310.18390,
  title  = {Entity Embeddings : Perspectives Towards an Omni-Modality Era for Large Language Models},
  author = {Eren Unlu and Unver Ciftci},
  journal= {arXiv preprint arXiv:2310.18390},
  year   = {2023}
}

Comments

9 pages, 5 figures

R2 v1 2026-06-28T13:04:11.511Z